Coding Summer School Tech Talk: How to make a (GameBoy) emulator? – by Petar Veličković Continue reading
Category Archives: Tutorials
Data Science Resources: Stellar Classification using Machine Learning
02/06/16 Tech Talk: Stellar Classification using Machine Learning – by Sandra Greiss Continue reading
Data Science Resources: Exploratory data analysis & interactive figures with Plotly
25/05/16 Tech Lab: Exploratory data analysis & interactive figures with Plotly – by Charles Ravarani Continue reading
Expanding your machine learning toolkit: Randomized search, computational budgets, and new algorithms
Introduction Previously, we wrote about some common trade-offs in machine learning and the importance of tuning models to your specific dataset. We demonstrated how to tune a random forest classifier using grid search, and how cross-validation can help avoid overfitting when tuning hyperparameters (HPs). In this follow-up post, you’ll beef up your machine learning toolbox … Continue reading
Introduction to Program Verification
19/04/16 Tech Talk: An Introduction to Program Verification – by Dominic Orchard As part of our series of Cambridge Coding Academy Tech Talks, this presentation will introduce an increasingly popular class of techniques called “model checking” approaches, covering models, logics, and music programming as an example. Continue reading
Feature Selection: methods and applications
Introduction In my previous blog post you can find an overview about how Big Data and Machine Learning are related to each other. The combination of the two can help us in finding answers to important questions across many fields of study. Here are some examples: which particle collisions help us learn more about the … Continue reading
How to build a computer program that can write
At the last Data Science bootcamp, the invited speaker Ekaterina Kochmar gave a fascinating and enlightening talk about recent advances in Natural Language Processing research. In the last part of her talk she briefly described how machines are now able to write using Language Models, but she didn’t have time to talk about those models … Continue reading
Scanning hyperspace: how to tune machine learning models
Introduction When doing machine learning using Python’s scikit-learn library, you can often get reasonable predictive performance by using out-of-the-box settings for your models. However, the payoff can be huge if you invest at least some time into tuning models to your specific problem and dataset. In the previous post, we explored the concepts of overfitting, … Continue reading
Data Science Talks: The Subjective Eye of Machine Vision
Miriam Redi, Yahoo Vision algorithms have achieved impressive performances in visual recognition. Nevertheless, an image is worth a thousand words, and not all these words refer to visible properties such as objects and scenes. Continue reading
Misleading modelling: overfitting, cross-validation, and the bias-variance trade-off
Introduction In this post you will get to grips with what is perhaps the most essential concept in machine learning: the bias-variance trade-off. The main idea here is that you want to create models that are as good at prediction as possible but that are still applicable to new data (i.e. they are generalizable). The … Continue reading
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